Astronauts must go through extensive training of their tasks in a simulated microgravity or reduced-gravity environment before they can perform the same tasks in space. Scientists and engineers also need simulated reduced-gravity facilities to study human performance and factors in space. This paper presents a novel design and prototype test of a multi-DOF reduced-gravity simulator which can be used for training astronauts and studying human factors in zero or partial gravity environment. Designed based on the passive static balancing technology, the simulator can passively compensate the gravity force applied to a human to any level from 0 to 100% at all the configurations within its workspace. Therefore, a person attached to the simulator can biomechanically feel like he/she were in a real reduced-gravity environment while doing a physical activity such as walking or jumping. A prototype of the simulator was developed to study the feasibility and dynamic performance of the system. The prototype study demonstrated the reduced-gravity simulation capability of the system. It also revealed some interesting findings which motivate further research in the future.

Statically-balanced technology is often used to compensate partial or full gravity force exerting on a mechanism in order to reduce the effort of driving the mechanism. Since such compensation is static, when a statically-balanced mechanism is used for dynamic applications, it will still have impedance (mechanical resistance to input motion) because the mechanism still subjects to the inertia forces although the gravity force has been compensated. Such a dynamic effect is undesirable for many applications, especially for those required to physically interact with humans and hence, the impedance property needs to be fully understood and possibly minimized in the design and/or operation of a statically-balanced mechanism. This paper studies the impedance property of passive statically-balanced mechanisms. Based on the study result, optimization strategies are proposed in order to optimize the operation of a statically-balanced mechanism. The strategies are then applied to a spring-based reduced-gravity simulation mechanism to figure out an optimal set of configurations in the workspace where the mechanism has the lowest impedance and the highest zero-gravity simulation fidelity.

No abstract

Statically-balanced mechanisms have been widely used for passive compensation of gravity loads in many applications including neurological rehabilitation and micro-/reduced-gravity simulation. For these applications, it is desirable that the used mechanism has minimal impedance the interacting human can feel. Impedance of a statically-balanced mechanism is contributed by many factors including the payload on the end-effector and the joint friction. This paper studies the relation between the end-effector impedance and the load-dependent joint friction for statically-balanced mechanisms. In the study a load dependent joint friction force model was developed. With the model, contribution of the end-effector load or payload on the joint friction can be evaluated, from which the end-effector impedance of the mechanism caused by the joint friction can be computed. The study results are applied to the analysis of a reduced-gravity simulator to evaluate the effect of the joint friction on the end-effector impedance of the mechanism. The findings of the study can help the assessment of the dynamic performance and also help the optimal design of statically-balanced mechanisms.

Mass center of a human body is not a fixed point on the human body because the inertia distribution of the human body changes with body posture. Real-time estimation of the location of human mass center is often required for many biomechanical or biomedical applications. This is not an easy task if the inertia properties of the human’s body segments are unknown. This paper presents a technique for estimating the trajectory of the human mass center based on a recently developed inertia properties identification technology which was derived based on the impulse-momentum principle. The proposed technique assumes a human body as a general treelike multibody system, such that the mass center of the human is predictable with the knowledge of the barycentric parameters of the human. The latter can be identified using inertia identification method. This technique is advantageous because it requires only the 3D motion capture data as its primary input and does not need to know the inertia and geometric parameters of individual body segments of the human. The paper presents a dynamic simulation based study of the proposed estimation technique and also describes an ongoing experimental testing.